CN105868267B - A kind of modeling method of mobile social networking user interest - Google Patents
A kind of modeling method of mobile social networking user interest Download PDFInfo
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- CN105868267B CN105868267B CN201610124887.4A CN201610124887A CN105868267B CN 105868267 B CN105868267 B CN 105868267B CN 201610124887 A CN201610124887 A CN 201610124887A CN 105868267 B CN105868267 B CN 105868267B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Abstract
The invention discloses a kind of modeling method of mobile social networking user interest, overall plan includes selection and acquisition, the representation of user interest model, the calculating of user interest degree weight, the storage of interest model and the associated algorithm of user interest information.The present invention can more accurately hold user interest, improve user experience, realize the personalized service of user and the accurate popularization of content.The modeling representation method of use, on the one hand, the items of interest and interested degree of representation method energy visual representation user；On the other hand, the advantages of representation method be the length of user interest expression be it is the same, be conducive to the foundation of dynamic model and the calculating of user's similarity, which can implement individualized content for content supplier and recommend provide foundation.
Description
Technical field
The present invention relates to the interest modeling methods of mobile social networking user a kind of, belong to mobile message technical field.
Background technique
It is more and more between people with the mobile intelligent terminals such as mobile phone, the fast development of wireless technology and 4G network
It is in communication with each other by handheld mobile device, and then has gradually formed mobile social networking (Mobile Social
Network, MSN).Many has attracted a large amount of user, such as present microblogging, wechat based on the application of social networking service
Deng.User can browse the information of each channel by mobile social networking platform, and enterprise can also market product and release information, move
Dynamic social networks deep infiltration daily life and business activity.
With the explosive growth of mobile social networking userbase and information content, on the one hand, growing information makes
It obtains people to be difficult to fast and accurately obtain the really necessary content wanted, on the other hand, for content supplier, differentiation is not added
Identical commercial activities information is pushed for all users and not only spends higher, but also easily causes user's dislike, information is brought to disturb
It disturbs.Therefore, for businessman and mobile platform, the interest based on user pushes its favorite content, can promote user's body
It tests, improves the acceptance level of information, net income increase.Based on this, the target of this paper establishes mobile social networking user interest mould
Type improves user experience, realizes the personalized service of user and the accurate popularization of content to accurately hold user interest.
Summary of the invention
The object of the present invention is to provide one kind can more accurately hold user interest, improves user experience, realizes user
Personalized service and content the mobile social networking user interest precisely promoted modeling method.
The technical solution adopted by the present invention are as follows:
A kind of modeling method of mobile social networking user interest, innovative point are: overall plan includes user interest
The selection and acquisition of information, the representation of user interest model, the calculating of user interest degree weight, the storage of interest model with
And associated algorithm, the specific steps of which are as follows:
1) selection and acquisition of interest information: the behavior generated during using mobile social networking by excavating user
Information is obtained and selected with resource, specifically includes the information issued by user itself, these information are to excavate user interest
The important sources of information；By the personal label of user, label describes the field of oneself hobby and concern with keyword, can be straight
Connect dominant acquisition user interest；Content is pushed by user comment and the other users of forwarding；
2) representation of user interest model: the source text set of user interest information is expressed as text D, and foundation is used for
The Hash dictionary of participle and statistics word frequency, extracts Feature Words, indicates user interest constitutive characteristic vector with vector space method, will use
The item of interest and weight at family are expressed as the vector in vector space；By the item of interest of user according to certain classification or the original of cluster
It then distinguishes, item of interest number is unsuitable huge, in order to avoid cause dimension excessively high in storage, the higher-dimension for causing matrix sparse is asked
Topic；
3) calculating of user interest degree weight: the calculating of interest-degree weight uses improved TF-IDF algorithm, and the algorithm is logical
Often it is used in text mining to assess a words for a copy of it file in a file set or a corpus
Significance level, improvement is based on the characteristic for considering mobile social networking, and certain events are objectively a large amount of in a short time
Forwarding, cause surrounding and watching for netizen, which is simultaneously not belonging to user's true interest expression, to give in the algorithm design of weight
With identification and correction；
Assuming that certain user, the information aggregate issued and forwarded within certain time is m, then certain item of interest weight of the user
Calculation formula are as follows:
Wherein, MsgjIndicate the j-th strip information that user delivers, Countij(Msgj) it is that item of interest i is mentioned in this information
Number, item of interest shared n；kallIt is good friend's sum of user's concern；kjIt is the number of users for forwarding the information；By the weight
Normalized can be obtained by the user that is indicated with degree of membership to the interest-degree of certain item of interest；
4) storage of interest model and associated algorithm: using relevant database storage mobile network user and
It pays close attention to the information content that good friend issues and forwards, and determines the item of interest and its correlated characteristic item of user, this is related to interest
Then the size of degree and the size of interest model establish item of interest dictionary and text data cleaning dictionary, write and store calculating
The algorithm of item of interest word frequency and interest-degree weight calculation, generally, database purchase table include user message table, customer relationship
Table, item of interest information table, category of interest table, user interest item word frequency list, user interest degree table；
Pseudo-code of the algorithm are as follows:
Input: information text set (such as microblogging) WB
Output: the interest-degree vector model of user
(1) FOR j=1TO m
(2) SW=Segment (WBj) // word segmentation processing
(3)END FOR
(4) T=Statistics (SWS) // extraction and statistics obtain characteristic item
(5) FOR i=1TO n
(6) FOR j=1TO m
(7)Countij=Statistics (Ti,WBjThe word frequency for the characteristic item for including in each microblogging of) // statistics
(8)wij=Weight (Countij, m, kall, kjThe weight for the item of interest for including in certain microblogging of) // calculate
(9)// obtain each item of interest weight of user
(10)// normalized obtains interest-degree
(11)END FOR
(12)END FOR
(13) W={ (T1,W1),(T2,W2),...,(Tn,Wn) // obtain the interest vector model of user.
Beneficial effects of the present invention are as follows:
The present invention can more accurately hold user interest, improve user experience, realize the personalized service of user and interior
The accurate popularization held.The modeling representation method of use, on the one hand, the items of interest and sense of representation method energy visual representation user
The degree of interest；On the other hand, the advantages of representation method be user interest expression length be it is the same, be conducive to dynamic analog
The foundation of type and the calculating of user's similarity, the model can implement individualized content recommendation for content supplier and provide foundation.
Detailed description of the invention
The present invention is described in further details with reference to the accompanying drawings and detailed description.
Fig. 1 is the overview flow chart of mobile social networking user interest of the present invention modeling.
Fig. 2 is the data flowchart of mobile social networking user interest of the present invention modeling.
Specific embodiment
A kind of mobile social networking user interest as shown in Figure 1, Figure 2, overall plan includes the choosing of user interest information
It selects and obtains, the representation of user interest model, the calculating of user interest degree weight, the storage of interest model and phase therewith
The algorithm of pass, the specific steps of which are as follows:
1) selection and acquisition of interest information: the behavior of user embodies the interest of user, excavates user and is using mobile agency
The behavior and resource generated in network development process is handed over, by taking microblogging as an example, user delivers microblogging, concern user, forwarding and comment microblogging
Equal behaviors are the sources for excavating user interest.The microblogging of user itself publication can significantly be related to own interests after collecting
Field is the important sources for excavating user interest information；If the personal label of user with keyword describe oneself hobby and
The field of concern, such as travelling, automobile, photography, cuisines, then can direct dominance acquisition user interest；User pays close attention to other micro-
Rich user, then the browsable microblogging to other users pushes content, may also comment on and forwards.Forwarding is that most social networks is special
The user behavior of sign, just because of user pays close attention to microblogging text information and can just forward；
2) item of interest of user is distinguished according to the principle of certain classification or cluster, item of interest number is unsuitable huge
Greatly, in order to avoid causing dimension excessively high in storage, cause the higher-dimension Sparse Problems of matrix.It is generated naturally according to User Activity in reality
Theme interest group, the user in this theme group belongs to a major class interest, such as sport, and deposits under the major class interest
In many small projects, such as football, basketball, swimming etc.；
3) modeling of user interest model uses vector space representation, and thinking is to indicate the item of interest of user and weight
At the vector in vector space, the source text set of user interest information is expressed as text D, establishes for segmenting and counting word frequency
Hash dictionary, extract Feature Words, user interest constitutive characteristic vector W={ (t1,w1),(t2,w2),...,(tn,wn), tiIt is
I-th of interest characteristics item in text set D, wiIt is the weight of this feature item.On the one hand, representation method energy visual representation user
Items of interest and interested degree；On the other hand, it is the same that the advantages of representation method, which is the length of user interest expression,
, be conducive to the foundation of dynamic model and the calculating of user's similarity.The model can implement individualized content for content supplier
Recommend to provide foundation；
4) storage of interest model and associated algorithm: the calculating of interest-degree weight is calculated using improved TF-IDF
Method, the algorithm are usually used in text mining to assess a words for its in a file set or a corpus
The significance level of middle text document, improvement are based on the characteristic for considering mobile social networking, and certain events are objectively in short-term
It is interior largely to be forwarded, surrounding and watching for netizen is caused, which is simultaneously not belonging to the true interest expression of user, in the algorithm of weight
Identification is given in design and is corrected；
Assuming that certain user, the information aggregate issued and forwarded within certain time is m, then certain item of interest weight of the user
Calculation formula is
Wherein, MsgjIndicate the j-th strip information that user delivers, Countij(Msgj) it is that item of interest i is mentioned in this information
Number, item of interest shared n；kallIt is good friend's sum of user's concern；kjIt is the number of users for forwarding the information；Item of interest is total
There are n.The weight normalized can be obtained by the user that is indicated with degree of membership to the interest-degree of certain item of interest.
Mobile network user is stored using relevant database and its pays close attention to the information content that good friend issues and forwards, really
Determine the item of interest and its correlated characteristic of user, this is related to the size of interest-degree and the size of interest model, then establishes interest
Item dictionary and text data clear up dictionary, write and store the algorithm for calculating item of interest word frequency and interest-degree weight calculation, overall
On, database purchase table includes user message table, User relationship table, item of interest information table, category of interest table, user interest item word
Frequency table, user interest degree table etc..
The above is a preferred embodiment of the present invention, and the scope of the invention cannot be limited thereby.It should refer to
Out, for those skilled in the art, modification or equivalent replacement of the technical solution of the present invention are made, all
Protection scope of the present invention is not departed from.
Claims (1)
1. a kind of modeling method of mobile social networking user interest, it is characterised in that: overall plan includes user interest information
Selection and acquisition, the representation of user interest model, the calculating of user interest degree weight, the storage of interest model and with
Relevant algorithm, the specific steps of which are as follows:
1) selection and acquisition of interest information: the behavior and money generated during using mobile social networking by excavating user
Source obtains and selects information, specifically includes the information issued by user itself, these information are to excavate user interest information
Important sources；By the personal label of user, label describes the field of oneself hobby and concern with keyword, can directly show
Property obtain user interest；Content is pushed by user comment and the other users of forwarding；
2) representation of user interest model: the source text set of user interest information is expressed as text D, establishes for segmenting
With the Hash dictionary of statistics word frequency, Feature Words are extracted, user interest constitutive characteristic vector are indicated with vector space method, by user's
Item of interest and weight are expressed as the vector in vector space；By the item of interest of user according to the principle of certain classification or cluster into
Row is distinguished, and item of interest number is unsuitable huge, in order to avoid causing dimension excessively high in storage, causes the higher-dimension Sparse Problems of matrix；
3) calculating of user interest degree weight: the calculating of interest-degree weight uses improved TF-IDF algorithm, which usually transports
To assess a words for the weight of a copy of it file in a file set or a corpus in text mining
Degree is wanted, improvement is based on the characteristic for considering mobile social networking, and certain events are objectively largely turned in a short time
Hair, causes surrounding and watching for netizen, which is simultaneously not belonging to the true interest expression of user, to give knowledge in the algorithm design of weight
Not with correction；
Assuming that certain user, the information bar number issued and forwarded within certain time is m, then certain item of interest weight calculation of the user
Formula are as follows:
Wherein, MsgjIndicate the j-th strip information that user delivers, Countij(Msgj) it is time that item of interest i is mentioned in this information
Number, item of interest are n shared；kallIt is good friend's sum of user's concern；kjIt is the number of users for forwarding the information；By the weight normalizing
Change processing can be obtained by the user that is indicated with degree of membership to the interest-degree of certain item of interest；
4) storage of interest model and associated algorithm: using relevant database storage mobile network user and its pass
It is poured in the information content that friend issues and forwards, determines the item of interest and its correlated characteristic item of user, this is related to interest-degree
Then the size of size and interest model establishes item of interest dictionary and text data cleaning dictionary, writes and store calculating interest
The algorithm of word frequency and interest-degree weight calculation, generally, database purchase table includes user message table, User relationship table, emerging
Interesting item information table, category of interest table, user interest item word frequency list, user interest degree table；
Pseudo-code of the algorithm are as follows:
Input: information text set (such as microblogging) WB
Output: the interest-degree vector model of user
(1) FOR j=1 TO m
(2) SW=Segment (WBj) // word segmentation processing
(3)END FOR
(4) T=Statistics (SWS) // extraction and statistics obtain characteristic item
(5) FOR i=1 TO n
(6) FOR j=1 TO m
(7)Countij=Statistics (Ti,WBjThe word frequency for the characteristic item for including in each microblogging of) // statistics
(8)wij=Weight (Countij, m, kall, kjThe weight for the item of interest for including in certain microblogging of) // calculate
(9)// obtain each item of interest weight of user
(10)// normalized obtains interest-degree
(11)END FOR
(12)END FOR
(13) W={ (T1,W1),(T2,W2),...,(Tn,Wn) // obtain the interest vector model of user.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106453348B (en) * | 2016-10-31 | 2019-11-15 | 南京邮电大学 | Based on the login authentication method of user interest in social networks |
| CN108205682B (en) * | 2016-12-19 | 2021-10-08 | 同济大学 | Collaborative filtering method for fusing content and behavior for personalized recommendation |
| CN106878392B (en) * | 2017-01-11 | 2021-09-07 | 浙江工商大学 | Student achievement-based online service method and device |
| CN107491491A (en) * | 2017-07-20 | 2017-12-19 | 西南财经大学 | A kind of media article for adapting to user interest change recommends method |
| CN108038097A (en) * | 2017-11-20 | 2018-05-15 | 西安电子科技大学 | System and method is built based on NLP social activity question and answer network user's interest capability model |
| CN108595630A (en) * | 2018-04-24 | 2018-09-28 | 中译语通科技股份有限公司 | A kind of user behavior data analysis model and its construction method |
| CN109325175A (en) * | 2018-08-23 | 2019-02-12 | 广东工业大学 | Merge the news push method, device and equipment of microblogging interest digging |
| CN111241821B (en) * | 2018-11-28 | 2023-04-28 | 杭州海康威视数字技术股份有限公司 | Method and device for determining behavior characteristics of user |
| CN111415328B (en) * | 2020-02-20 | 2023-07-11 | 北京三快在线科技有限公司 | Method and device for determining article analysis data and electronic equipment |
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